//- 💫 DOCS > USAGE > PROCESSING PIPELINES > CUSTOM COMPONENTS

p
    |  A component receives a #[code Doc] object and can modify it – for example,
    |  by using the current weights to make a prediction and set some annotation
    |  on the document. By adding a component to the pipeline, you'll get access
    |  to the #[code Doc] at any point #[strong during processing] – instead of
    |  only being able to modify it afterwards.

+aside-code("Example").
    def my_component(doc):
        # do something to the doc here
        return doc

+table(["Argument", "Type", "Description"])
    +row
        +cell #[code doc]
        +cell #[code Doc]
        +cell The #[code Doc] object processed by the previous component.

    +row("foot")
        +cell returns
        +cell #[code Doc]
        +cell The #[code Doc] object processed by this pipeline component.

p
    |  Custom components can be added to the pipeline using the
    |  #[+api("language#add_pipe") #[code add_pipe]] method. Optionally, you
    |  can either specify a component to add it #[strong before or after], tell
    |  spaCy to add it #[strong first or last] in the pipeline, or define a
    |  #[strong custom name]. If no name is set and no #[code name] attribute
    |  is present on your component, the function name is used.

+code("Adding pipeline components").
    def my_component(doc):
        print("After tokenization, this doc has %s tokens." % len(doc))
        if len(doc) &lt; 10:
            print("This is a pretty short document.")
        return doc

    nlp = spacy.load('en')
    nlp.add_pipe(my_component, name='print_info', first=True)
    print(nlp.pipe_names) # ['print_info', 'tagger', 'parser', 'ner']
    doc = nlp(u"This is a sentence.")

p
    |  Of course, you can also wrap your component as a class to allow
    |  initialising it with custom settings and hold state within the component.
    |  This is useful for #[strong stateful components], especially ones which
    |  #[strong depend on shared data].

+code.
    class MyComponent(object):
        name = 'print_info'

        def __init__(vocab, short_limit=10):
            self.vocab = nlp.vocab
            self.short_limit = short_limit

        def __call__(doc):
            if len(doc) &lt; self.short_limit:
                print("This is a pretty short document.")
            return doc

    my_component = MyComponent(nlp.vocab, short_limit=25)
    nlp.add_pipe(my_component, first=True)

+h(3, "custom-components-attributes")
    |  Extension attributes on #[code Doc], #[code Span] and #[code Token]
    +tag-new(2)

p
    |  As of v2.0, spaCy allows you to set any custom attributes and methods
    |  on the #[code Doc], #[code Span] and #[code Token], which become
    |  available as #[code Doc._], #[code Span._] and #[code Token._] – for
    |  example, #[code Token._.my_attr]. This lets you store additional
    |  information relevant to your application, add new features and
    |  functionality to spaCy, and implement your own models trained with other
    |  machine learning libraries. It also lets you take advantage of spaCy's
    |  data structures and the #[code Doc] object as the "single source of
    |  truth".

+aside("Why ._?")
    |  Writing to a #[code ._] attribute instead of to the #[code Doc] directly
    |  keeps a clearer separation and makes it easier to ensure backwards
    |  compatibility. For example, if you've implemented your own #[code .coref]
    |  property and spaCy claims it one day, it'll break your code. Similarly,
    |  just by looking at the code, you'll immediately know what's built-in and
    |  what's custom – for example, #[code doc.sentiment] is spaCy, while
    |  #[code doc._.sent_score] isn't.

p
    |  There are three main types of extensions, which can be defined using the
    |  #[+api("doc#set_extension") #[code Doc.set_extension]],
    |  #[+api("span#set_extension") #[code Span.set_extension]] and
    |  #[+api("token#set_extension") #[code Token.set_extension]] methods.

+list("numbers")
    +item #[strong Attribute extensions].
        |  Set a default value for an attribute, which can be overwritten
        |  manually at any time. Attribute extensions work like "normal"
        |  variables and are the quickest way to store arbitrary information
        |  on a #[code Doc], #[code Span] or #[code Token]. Attribute defaults
        |  behaves just like argument defaults
        |  #[+a("http://docs.python-guide.org/en/latest/writing/gotchas/#mutable-default-arguments") in Python functions],
        |  and should not be used for mutable values like dictionaries or lists.

        +code-wrapper
            +code.
                Doc.set_extension('hello', default=True)
                assert doc._.hello
                doc._.hello = False

    +item #[strong Property extensions].
        |  Define a getter and an optional setter function. If no setter is
        |  provided, the extension is immutable. Since the getter and setter
        |  functions are only called when you #[em retrieve] the attribute,
        |  you can also access values of previously added attribute extensions.
        |  For example, a #[code Doc] getter can average over #[code Token]
        |   attributes. For #[code Span] extensions, you'll almost always want
        |  to use a property – otherwise, you'd have to write to
        |  #[em every possible] #[code Span] in the #[code Doc] to set up the
        |  values correctly.

        +code-wrapper
            +code.
                Doc.set_extension('hello', getter=get_hello_value, setter=set_hello_value)
                assert doc._.hello
                doc._.hello = 'Hi!'

    +item #[strong Method extensions].
        |  Assign a function that becomes available as an object method. Method
        |  extensions are always immutable. For more details and implementation
        |  ideas, see
        |  #[+a("/usage/examples#custom-components-attr-methods") these examples].

        +code-wrapper
            +code.o-no-block.
                Doc.set_extension('hello', method=lambda doc, name: 'Hi {}!'.format(name))
                assert doc._.hello('Bob') == 'Hi Bob!'

p
    |  Before you can access a custom extension, you need to register it using
    |  the #[code set_extension] method on the object you want
    |  to add it to, e.g. the #[code Doc]. Keep in mind that extensions are
    |  always #[strong added globally] and not just on a particular instance.
    |  If an attribute of the same name
    |  already exists, or if you're trying to access an attribute that hasn't
    |  been registered, spaCy will raise an #[code AttributeError].

+code("Example").
    from spacy.tokens import Doc, Span, Token

    fruits = ['apple', 'pear', 'banana', 'orange', 'strawberry']
    is_fruit_getter = lambda token: token.text in fruits
    has_fruit_getter = lambda obj: any([t.text in fruits for t in obj])

    Token.set_extension('is_fruit', getter=is_fruit_getter)
    Doc.set_extension('has_fruit', getter=has_fruit_getter)
    Span.set_extension('has_fruit', getter=has_fruit_getter)

+aside-code("Usage example").
    doc = nlp(u"I have an apple and a melon")
    assert doc[3]._.is_fruit      # get Token attributes
    assert not doc[0]._.is_fruit
    assert doc._.has_fruit        # get Doc attributes
    assert doc[1:4]._.has_fruit   # get Span attributes

p
    |  Once you've registered your custom attribute, you can also use the
    |  built-in #[code set], #[code get] and #[code has] methods to modify and
    |  retrieve the attributes. This is especially useful it you want to pass in
    |  a string instead of calling #[code doc._.my_attr].

+table(["Method", "Description", "Valid for", "Example"])
    +row
        +cell #[code ._.set()]
        +cell Set a value for an attribute.
        +cell Attributes, mutable properties.
        +cell #[code.u-break token._.set('my_attr', True)]

    +row
        +cell #[code ._.get()]
        +cell Get the value of an attribute.
        +cell Attributes, mutable properties, immutable properties, methods.
        +cell #[code.u-break my_attr = span._.get('my_attr')]

    +row
        +cell #[code ._.has()]
        +cell Check if an attribute exists.
        +cell Attributes, mutable properties, immutable properties, methods.
        +cell #[code.u-break doc._.has('my_attr')]

+infobox("How the ._ is implemented")
    |  Extension definitions – the defaults, methods, getters and setters you
    |  pass in to #[code set_extension] – are stored in class attributes on the
    |  #[code Underscore] class. If you write to an extension attribute, e.g.
    |  #[code doc._.hello = True], the data is stored within the
    |  #[+api("doc#attributes") #[code Doc.user_data]] dictionary. To keep the
    |  underscore data separate from your other dictionary entries, the string
    |  #[code "._."] is placed before the name, in a tuple.

+h(4, "component-example1") Example: Custom sentence segmentation logic

p
    |  Let's say you want to implement custom logic to improve spaCy's sentence
    |  boundary detection. Currently, sentence segmentation is based on the
    |  dependency parse, which doesn't always produce ideal results. The custom
    |  logic should therefore be applied #[strong after] tokenization, but
    |  #[strong before] the dependency parsing – this way, the parser can also
    |  take advantage of the sentence boundaries.

+code.
    def sbd_component(doc):
        for i, token in enumerate(doc[:-2]):
            # define sentence start if period + titlecase token
            if token.text == '.' and doc[i+1].is_title:
                doc[i+1].sent_start = True
        return doc

    nlp = spacy.load('en')
    nlp.add_pipe(sbd_component, before='parser')  # insert before the parser

+h(4, "component-example2")
    |  Example: Pipeline component for entity matching and tagging with
    |  custom attributes

p
    |  This example shows how to create a spaCy extension that takes a
    |  terminology list (in this case, single- and multi-word company names),
    |  matches the occurences in a document, labels them as #[code ORG] entities,
    |  merges the tokens and sets custom #[code is_tech_org] and
    |  #[code has_tech_org] attributes. For efficient matching, the example uses
    |  the #[+api("phrasematcher") #[code PhraseMatcher]] which accepts
    |  #[code Doc] objects as match patterns and works well for large
    |  terminology lists. It also ensures your patterns will always match, even
    |  when you customise spaCy's tokenization rules. When you call #[code nlp]
    |  on a text, the custom pipeline component is applied to the #[code Doc]

+github("spacy", "examples/pipeline/custom_component_entities.py", 500)

p
    |  Wrapping this functionality in a
    |  pipeline component allows you to reuse the module with different
    |  settings, and have all pre-processing taken care of when you call
    |  #[code nlp] on your text and receive a #[code Doc] object.

+h(4, "component-example3")
    |  Example: Pipeline component for GPE entities and country meta data via a
    |  REST API

p
    |  This example shows the implementation of a pipeline component
    |  that fetches country meta data via the
    |  #[+a("https://restcountries.eu") REST Countries API] sets entity
    |  annotations for countries, merges entities into one token and
    |  sets custom attributes on the #[code Doc], #[code Span] and
    |  #[code Token] – for example, the capital, latitude/longitude coordinates
    |  and even the country flag.

+github("spacy", "examples/pipeline/custom_component_countries_api.py", 500)

p
    |  In this case, all data can be fetched on initialisation in one request.
    |  However, if you're working with text that contains incomplete country
    |  names, spelling mistakes or foreign-language versions, you could also
    |  implement a #[code like_country]-style getter function that makes a
    |  request to the search API endpoint and returns the best-matching
    |  result.

+h(4, "custom-components-usage-ideas") Other usage ideas

+list
    +item
        |  #[strong Adding new features and hooking in models]. For example,
        |  a sentiment analysis model, or your preferred solution for
        |  lemmatization or sentiment analysis. spaCy's built-in tagger,
        |  parser and entity recognizer respect annotations that were already
        |  set on the #[code Doc] in a previous step of the pipeline.
    +item
        |  #[strong Integrating other libraries and APIs]. For example, your
        |  pipeline component can write additional information and data
        |  directly to the #[code Doc] or #[code Token] as custom attributes,
        |  while making sure no information is lost in the process. This can
        |  be output generated by other libraries and models, or an external
        |  service with a REST API.
    +item
        |  #[strong Debugging and logging]. For example, a component which
        |  stores and/or exports relevant information about the current state
        |  of the processed document, and insert it at any point of your
        |  pipeline.

+infobox("Developing third-party extensions")
    |  The new pipeline management and custom attributes finally make it easy
    |  to develop your own spaCy extensions and plugins and share them with
    |  others. Extensions can claim their own #[code ._] namespace and exist as
    |  standalone packages. If you're developing a tool or library and want to
    |  make it easy for others to use it with spaCy and add it to their
    |  pipeline, all you have to do is expose a function that takes a
    |  #[code Doc], modifies it and returns it. For more details and
    |  #[strong best practices], see the section on
    |  #[+a("#extensions") developing spaCy extensions].

+h(3, "custom-components-user-hooks") User hooks

p
    |  While it's generally recommended to use the #[code Doc._], #[code Span._]
    |  and #[code Token._] proxies to add your own custom attributes, spaCy
    |  offers a few exceptions to allow #[strong customising the built-in methods]
    |  like #[+api("doc#similarity") #[code Doc.similarity]] or
    |  #[+api("doc#vector") #[code Doc.vector]]. with your own hooks, which can
    |  rely on statistical models you train yourself. For instance, you can
    |  provide your own on-the-fly sentence segmentation algorithm or document
    |  similarity method.

p
    |  Hooks let you customize some of the behaviours of the #[code Doc],
    |  #[code Span] or #[code Token] objects by adding a component to the
    |  pipeline. For instance, to customize the
    |  #[+api("doc#similarity") #[code Doc.similarity]] method, you can add a
    |  component that sets a custom function to
    |  #[code doc.user_hooks['similarity']]. The built-in #[code Doc.similarity]
    |  method will check the #[code user_hooks] dict, and delegate to your
    |  function if you've set one. Similar results can be achieved by setting
    |  functions to #[code Doc.user_span_hooks] and #[code Doc.user_token_hooks].

+aside("Implementation note")
    |  The hooks live on the #[code Doc] object because the #[code Span] and
    |  #[code Token] objects are created lazily, and don't own any data. They
    |  just proxy to their parent #[code Doc]. This turns out to be convenient
    |  here — we only have to worry about installing hooks in one place.

+table(["Name", "Customises"])
    +row
        +cell #[code user_hooks]
        +cell
            +api("doc#vector") #[code Doc.vector]
            +api("doc#has_vector") #[code Doc.has_vector]
            +api("doc#vector_norm") #[code Doc.vector_norm]
            +api("doc#sents") #[code Doc.sents]

    +row
        +cell #[code user_token_hooks]
        +cell
            +api("token#similarity") #[code Token.similarity]
            +api("token#vector") #[code Token.vector]
            +api("token#has_vector") #[code Token.has_vector]
            +api("token#vector_norm") #[code Token.vector_norm]
            +api("token#conjuncts") #[code Token.conjuncts]

    +row
        +cell #[code user_span_hooks]
        +cell
            +api("span#similarity") #[code Span.similarity]
            +api("span#vector") #[code Span.vector]
            +api("span#has_vector") #[code Span.has_vector]
            +api("span#vector_norm") #[code Span.vector_norm]
            +api("span#root") #[code Span.root]

+code("Add custom similarity hooks").
    class SimilarityModel(object):
        def __init__(self, model):
            self._model = model

        def __call__(self, doc):
            doc.user_hooks['similarity'] = self.similarity
            doc.user_span_hooks['similarity'] = self.similarity
            doc.user_token_hooks['similarity'] = self.similarity

        def similarity(self, obj1, obj2):
            y = self._model([obj1.vector, obj2.vector])
            return float(y[0])
